Sample draws from a tidy-format data frame of draws

Given a tidy-format data frame of draws with a column indexing each draw, subsample the data frame to a given size based on a column indexing draws, ensuring that rows in sub-groups of a grouped data frame are sampled from the same draws.

sample_draws(data, ndraws, draw = ".draw", seed = NULL)

Arguments

data

Data frame to sample from

ndraws

The number of draws to return, or NULL to return all draws.

draw

The name of the column indexing the draws; default ".draw".

seed

A seed to use when subsampling draws (i.e. when ndraws is not NULL).

Details

sample_draws() makes it easier to sub-sample a grouped, tidy-format data frame of draws. On a grouped data frame, the naive approach of using filter with the .draw column will give incorrect results as it will select a different sample within each group. sample_draws() ensures the same sample is selected within each group.

Author

Matthew Kay

Examples

# \dontrun{

library(ggplot2)
library(dplyr)
library(brms)
library(modelr)

theme_set(theme_light())

m_mpg = brm(mpg ~ hp * cyl, data = mtcars,
  # 1 chain / few iterations just so example runs quickly
  # do not use in practice
  chains = 1, iter = 500)
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:   1 / 500 [  0%]  (Warmup)
#> Chain 1: Iteration:  50 / 500 [ 10%]  (Warmup)
#> Chain 1: Iteration: 100 / 500 [ 20%]  (Warmup)
#> Chain 1: Iteration: 150 / 500 [ 30%]  (Warmup)
#> Chain 1: Iteration: 200 / 500 [ 40%]  (Warmup)
#> Chain 1: Iteration: 250 / 500 [ 50%]  (Warmup)
#> Chain 1: Iteration: 251 / 500 [ 50%]  (Sampling)
#> Chain 1: Iteration: 300 / 500 [ 60%]  (Sampling)
#> Chain 1: Iteration: 350 / 500 [ 70%]  (Sampling)
#> Chain 1: Iteration: 400 / 500 [ 80%]  (Sampling)
#> Chain 1: Iteration: 450 / 500 [ 90%]  (Sampling)
#> Chain 1: Iteration: 500 / 500 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.128 seconds (Warm-up)
#> Chain 1:                0.045 seconds (Sampling)
#> Chain 1:                0.173 seconds (Total)
#> Chain 1: 
#> Warning: The largest R-hat is 1.07, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess

# draw 100 fit lines from the posterior and overplot them
mtcars %>%
  group_by(cyl) %>%
  data_grid(hp = seq_range(hp, n = 101)) %>%
  add_epred_draws(m_mpg) %>%
  # NOTE: only use sample_draws here when making spaghetti plots; for
  # plotting intervals it is always best to use all draws
  sample_draws(100) %>%
  ggplot(aes(x = hp, y = mpg, color = ordered(cyl))) +
  geom_line(aes(y = .epred, group = paste(cyl, .draw)), alpha = 0.25) +
  geom_point(data = mtcars)


# }